Completed Local Ternary Pattern for Rotation Invariant Texture Classification

Despite the fact that the two texture descriptors, the completed modeling of Local Binary Pattern (CLBP) and the Completed Local Binary Count (CLBC), have achieved a remarkable accuracy for invariant rotation texture classification, they inherit some Local Binary Pattern (LBP) drawbacks. The LBP is sensitive to noise, and different patterns of LBP may be classified into the same class that reduces its discriminating property. Although, the Local Ternary Pattern (LTP) is proposed to be more robust to noise than LBP, however, the latter’s weakness may appear with the LTP as well as with LBP. In this paper, a novel completed modeling of the Local Ternary Pattern (LTP) operator is proposed to overcome both LBP drawbacks, and an associated completed Local Ternary Pattern (CLTP) scheme is developed for rotation invariant texture classification. The experimental results using four different texture databases show that the proposed CLTP achieved an impressive classification accuracy as compared to the CLBP and CLBC descriptors

Ekstraksi Ciri Tekstur Menggunakan Improved Completed Robust Local Binary Pattern Untuk Klasifikasi Citra Batik

Untuk membantu proses pendokumentasian citra batik, dibutuhkan sistem klasifikasi yang cukup handal dalam mengklasifikasi dan mengidentifikasi citra batik. Salah satu bagian penting dari sistem klasifikasi adalah metode ekstraksi ciri. Pemilihan metode ekstraksi ciri yang tepat sangat dibutuhkan agar dapat mencapai akurasi yang tinggi pada sistem klasifikasi. Metode ekstraksi ciri tekstur menjadi pilihan pada sistem klasifikasi kali ini, karena batik direpresentasikan berdasarkan motif dasarnya. Salah satu metode ekstraksi ciri tekstur yang handal adalah Local Binary Pattern (LBP). LBP adalah metode yang sederhana namun efisien dalam merepresentasikan ciri, serta gray-scale invariant. Beberapa penelitian telah diajukan untuk meningkatkan kinerja LBP. Salah satunya adalah Completed Robust Local Binary Pattern (CRLBP), diusulkan oleh Zhao untuk mengatasi kelemahan CLBP yang sensitif terhadap noise. Namun, CRLBP tidak invariant terhadap rotasi. Dari permasalahan tersebut, penelitian kali ini mengusulkan pendekatan baru dari CRLBP, dengan cara menyisipkan metode LBPROT ke dalam algoritma CRLBP. LBPROT adalah salah satu metode yang diusulkan untuk memperbaiki kelemahan LBP agar invariant terhadap rotasi. Pendekatan yang disebut di atas dinamakan Improved Completed Robust Local Binary Pattern (ICRLBP). ICRLBP memiliki metode dasar yang sama dengan CRLBP. ICRLB memiliki 3 histogram ciri yaitu ICRLBP_Sign, ICRLBP_Magnitude, dan ICRLBP_Center. Algoritma LBPROT disisipkan setelah sign vector dan magnitude vector diperoleh. LBPROT mencari kombinasi nilai biner yang terkecil dari nilai biner sign vector dan magnitude vector pada setiap piksel. Kombinasi nilai biner terkecil tersebut dikonversi ke bilangan desimal. Dari nilai desimal tersebut histogram ciri ICRLBP disusun. Selanjutnya, Histogram ciri ICRLBP menjadi data masukkan ke klasifikasi Probabilistic Neural Network. Kinerja sistem diukur menggunakan akurasi. Hasil uji coba menunjukkan bahwa ICRLBP dapat meningkatkan akurasi sebesar 17,39% dan lebih cepat 300 kali lipa t dari CRLBP pada dataset Batik. Hal ini menunjukkan bahwa ICRLBP lebih handal dibandingkan CRLBP. ============================================================================================================================== Assisting the process of batik image documentation, a reliable classification system is needed. One important part in the classification system is the feature extraction method. Selecting an appropriate feature extraction method is an urgent issue in order to achieve high accuracy in the classification system. Texture feature extraction method is choosen at this study, because batik can be represented by its basic pattern or motif. One of reliable texture feature extraction methods is Local Binary Pattern (LBP). LBP is a simple but efficient method and gray-scale invariant, namely it is not affected at uneven illumination issue on the image, because LBP describes texture locally. Some studies have been proposed to improve the performance of LBP, such as Completed Robust Local Binary Pattern (CRLBP). CRLBP is proposed by Zhao to overcome the weaknesses of CLBP that sensitive to noise. However, CRLBP is not invariant to rotation. From that problem, in this study, a new approach of CRLBP is proposed. CRLBP algor ithm will be inserted by LBPROT algorithm. LBPROT is one of improved LBP methods that proposed to overcome the LBP weakness which is not rotation invariant. The approach is called Improved Completed Robust Local Binary Pattern (ICRLBP). ICRLBP has the same basic method to CRLBP. ICRLBP has three feature histograms namely ICRLBP_Sign, ICRLBP_Magnitude, and ICRLBP_Center. After sign vector and magnitude vector is gotten, LBPROT algorithm is inserted. LBPROT looks for the smallest binary combination value of sign binary vector and magnitude binary vector in each piksel. Futhermore, the smallest binary combination value is converted to decimal. That decimal value is used to build the ICRLBP histograms. ICRLBP histograms as input data is fed into classification system using Probabilistic Neural Network. The performance of classification system is evaluated using accuracy. The result experiments show that the accuracy and the speed of ICRLBP increased by 17.39% and 300 times for Batik datasets, respectively. It sho that ICRLBP is proven can improve the performance of CRLBP

A Novel Adaptive LBP-Based Descriptor for Color Image Retrieval

In this paper, we present two approaches to extract discriminative features for color image retrieval. The proposed local texture descriptors, based on Radial Mean Local Binary Pattern (RMLBP), are called Color RMCLBP (CRMCLBP) and Prototype Data Model (PDM). RMLBP is a robust to noise descriptor which has been proposed to extract texture features of gray scale images for texture classification. For the first descriptor, the Radial Mean Completed Local Binary Pattern is applied to channels of the color space, independently. Then, the final descriptor is achieved by concatenating the histogram of the CRMCLBP_S/M/C component of each channel. Moreover, to enhance the performance of the proposed method, the Particle Swarm Optimization (PSO) algorithm is used for feature weighting. The second proposed descriptor, PDM, uses the three outputs of CRMCLBP (CRMCLBP_S, CRMCLBP_M, CRMCLBP_C) as discriminative features for each pixel of a color image. Then, a set of representative feature vectors are selected from each image by applying k-means clustering algorithm. This set of selected prototypes are compared by means of a new similarity measure to find the most relevant images. Finally, the weighted versions of PDM is constructed using PSO algorithm. Our proposed methods are tested on Wang, Corel-5k, Corel-10k and Holidays datasets. The results show that our proposed methods makes an admissible tradeoff between speed and retrieval accuracy. The first descriptor enhances the state-of-the-art color texture descriptors in both aspects. The second one is a very fast retrieval algorithm which extracts discriminative features

Scale Selective Extended Local Binary Pattern for Texture Classification

In this paper, we propose a new texture descriptor, scale selective extended local binary pattern (SSELBP), to characterize texture images with scale variations. We first utilize multi-scale extended local binary patterns (ELBP) with rotation-invariant and uniform mappings to capture robust local micro- and macro-features. Then, we build a scale space using Gaussian filters and calculate the histogram of multi-scale ELBPs for the image at each scale. Finally, we select the maximum values from the corresponding bins of multi-scale ELBP histograms at different scales as scale-invariant features. A comprehensive evaluation on public texture databases (KTH-TIPS and UMD) shows that the proposed SSELBP has high accuracy comparable to state-of-the-art texture descriptors on gray-scale-, rotation-, and scale-invariant texture classification but uses only one-third of the feature dimension.Comment: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 201

Improving Texture Categorization with Biologically Inspired Filtering

Within the domain of texture classification, a lot of effort has been spent on local descriptors, leading to many powerful algorithms. However, preprocessing techniques have received much less attention despite their important potential for improving the overall classification performance. We address this question by proposing a novel, simple, yet very powerful biologically-inspired filtering (BF) which simulates the performance of human retina. In the proposed approach, given a texture image, after applying a DoG filter to detect the "edges", we first split the filtered image into two "maps" alongside the sides of its edges. The feature extraction step is then carried out on the two "maps" instead of the input image. Our algorithm has several advantages such as simplicity, robustness to illumination and noise, and discriminative power. Experimental results on three large texture databases show that with an extremely low computational cost, the proposed method improves significantly the performance of many texture classification systems, notably in noisy environments. The source codes of the proposed algorithm can be downloaded from https://sites.google.com/site/nsonvu/code.Comment: 11 page

Generalized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted ncomponent of this work in other works.Efficient defect classification is one of the most important preconditions to achieve online quality inspection for hot-rolled strip steels. It is extremely challenging owing to various defect appearances, large intraclass variation, ambiguous interclass distance, and unstable gray values. In this paper, a generalized completed local binary patterns (GCLBP) framework is proposed. Two variants of improved completed local binary patterns (ICLBP) and improved completed noise-invariant local-structure patterns (ICNLP) under the GCLBP framework are developed for steel surface defect classification. Different from conventional local binary patterns variants, descriptive information hidden in nonuniform patterns is innovatively excavated for the better defect representation. This paper focuses on the following aspects. First, a lightweight searching algorithm is established for exploiting the dominant nonuniform patterns (DNUPs). Second, a hybrid pattern code mapping mechanism is proposed to encode all the uniform patterns and DNUPs. Third, feature extraction is carried out under the GCLBP framework. Finally, histogram matching is efficiently accomplished by simple nearest-neighbor classifier. The classification accuracy and time efficiency are verified on a widely recognized texture database (Outex) and a real-world steel surface defect database [Northeastern University (NEU)]. The experimental results promise that the proposed method can be widely applied in online automatic optical inspection instruments for hot-rolled strip steel.Peer reviewe

Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns

Developments in defect descriptors and computer vision-based algorithms for automatic optical inspection (AOI) allows for further development in image-based measurements. Defect classification is a vital part of an optical-imaging-based surface quality measuring instrument. The high-speed production rhythm of hot continuous rolling requires an ultra-rapid response to every component as well as algorithms in AOI instrument. In this paper, a simple, fast, yet robust texture descriptor, namely selectively dominant local binary patterns (SDLBPs), is proposed for defect classification. First, an intelligent searching algorithm with a quantitative thresholding mechanism is built to excavate the dominant non-uniform patterns (DNUPs). Second, two convertible schemes of pattern code mapping are developed for binary encoding of all uniform patterns and DNUPs. Third, feature extraction is carried out under SDLBP framework. Finally, an adaptive region weighting method is built for further strengthening the original nearest neighbor classifier in the feature matching stage. The extensive experiments carried out on an open texture database (Outex) and an actual surface defect database (Dragon) indicates that our proposed SDLBP yields promising performance on both classification accuracy and time efficiencyPeer reviewe